Computation EE Fairness for a UAV-Enabled Wireless Powered MEC Network With Hybrid Passive and Active Transmissions

Energy-efficient computation is an inevitable trend for unmanned aerial vehicles (UAVs)-enabled wireless powered mobile edge computing (MEC), while it has not been investigated when the hybrid passive and active transmissions (ATs) are considered for Internet of Things (IoT) nodes’ task offloading. In this article, we study the computation energy efficiency (EE) fairness among IoT nodes in a UAV-enabled wireless powered MEC network with hybrid passive and ATs, where the UAV serves as a dynamic energy source to support IoT nodes for backscatter communication (BackCom) and AT. Specifically, we formulate an optimization problem to maximize the computation EE of the worst IoT node by jointly optimizing the UAV’s transmit power and trajectory, the IoT nodes’ BackCom time and reflection coefficients, the IoT nodes’ AT power and time, as well as the IoT nodes’ local computing time and frequencies. The formulated problem is highly nonconvex and difficult to be solved optimally. To address it, we first obtain the closed-form expressions for the UAV’s transmit power and the IoT nodes’ local computing time by means of the proof by contradiction to simplify the problem, and then propose a Dinkelbach-based iterative algorithm to obtain the solution of other optimization variables. Specifically, based on the Dinkelbach’s method, the original fractional problem is transformed into the problem with the subtractive objective function. Then we further decouple the transformed problem into two subproblems based on the block-coordinated-decent (BCD) method and solve the transformed problem by the proposed BCD-based iterative algorithm, where the above two subproblems are solved by means of the existing convex optimization tools and the proposed successive convex approximation (SCA)-based iterative algorithm alternatively. Simulation results show that the proposed algorithms have a fast rate of convergence and that the proposed scheme outperforms other baseline schemes in terms of the computation EE fairness.